We may be upon predictive analytics’ moment in higher education, with student retention as its “killer app”. Institutions of every type – from 4-year publics to 4-year privates and community colleges – are acquiring commercial systems or building their own to mine a lengthening online audit trail of student data for everything from student services portal logins to LMS activity to digital textbook interactions.

Although analytics has been applied to recruiting data to predict enrollments for much of the last 10 years, only recently has interest grown to apply it to student retention. Specifically, we most often hear about four retention-related uses of predictive analytics:

Eduventures recently conducted interviews with institutional executives experienced in applying predictive analytics along the student life cycle, and all reported measurably improved outcomes in recruiting, retention, and/or resource utilization as well. The implications are a more efficient use of resources and more students staying in school.

Not convinced? Consider the following:

The national average freshman-to-sophomore retention rate is approximately 75%. This means that about one-quarter of the students who started in fall 2012 will not return to their freshman institution in fall 2013; therefore, an institution that enrolled 5,000 freshmen for fall 2012 can expect to lose 1,250 of them. At a median acquisition cost of $2,185, this implies a loss of $2,731,250 from increased costs of acquisition alone. When factoring in five subsequent years of foregone tuition and fees for each of those 1,250 non-returning freshmen, the costs of poor retention are high. Clearly, any tool that will help colleges and universities make data-driven decisions about which students are likely to persist will, over time, lead to dramatic improvements in efficiency and effectiveness – not to mention helping students graduate!

Although the jury is still out, we also see evidence that commercial providers are delivering systems that are not only more advanced in terms of data-mining capability and fine-tuned analytics algorithms but also in terms of being easier to use and “learning” with use. The goal – of incumbent and up-start system providers alike – has been to make predictive analytics as easy to use by the non-IT functional user as possible. And they are making progress, even if there remains a distance to go.

With so many institutions reporting success – and more joining them in making the attempt – we see ample reason to believe that predictive analytics is poised to break out of the low levels of penetration it’s had in academe. And more students will stay in school.